文档级关系抽取技术研究综述

Review of Document-level Relation Extraction Techniques

关系抽取是信息抽取研究的重要方向,已逐步从句子级扩展到了文档级。与句子相比,文档通常蕴含更多的关系事实,可为知识库构建、信息检索和语义分析等提供更多的信息支持。然而,文档级关系抽取复杂度更高,难度更大,目前缺乏较为系统全面的梳理和总结。为更好地促进文档级关系抽取的深入研究与发展,文中对已有技术和方法进行了综合深入分析,从数据预处理方式和核心算法角度,将已有文档级关系抽取研究大致分为基于树、基于序列和基于图3种类别;在此基础上,分析描述了各类研究中的部分典型方法、最新进展以及存在的不足;同时,介绍了现有研究中部分常用数据集和性能评价指标,并列出了已有部分典型方法的具体性能;最后,对现有文档级关系抽取研究存在的问题进行了分析和总结,指出了未来可能的发展趋势及可进一步深入关注的研究方向。

Relation extraction(RE)is an essential direction of information extraction research,it gradually expanding from sentence to document-level.Compared with sentences,documents usually contain more relation facts,providing more information for knowledge base construction,information retrieval,and semantic analysis.However,document-level relation extraction is more complex and challenging,and there is currently a lack of systematic and comprehensive sorting and summary.To better promote the development of document-level relation extraction,this paper carries out a comprehensive and in-depth analysis of the existing technologies and methods.From the perspective of data preprocessing methods and core algorithms,it classifies the existing methods into three types,including tree-based,sequence-based,and graph-based.Based on this,Relation extraction by category analyzes and describes some typical methods,the latest progress and shortcomings.At the same time,it introduces some corpus,evaluation metrics and some typical methods.Finally,the existing problems in document-level relation extraction research are analyzed and summarized,and the possible future development trends and research directions are discussed.